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Objective:To propose a markerless beam's eye view (BEV) tumor tracking algorithm, which can be applied to megavolt (MV) images with poor image quality, multi-leaf collimator (MLC) occlusion and non-rigid deformation.Methods:Window template matching, image structure transformation and demons non-rigid registration method were used to solve the registration problem in MV images. The quality assurance (QA) plan was generated in the phantom and executed after manually setting the treatment offset on the accelerator, and 682 electronic portal imaging device (EPID) images in the treatment process were collected as fixed images. Meanwhile, the digitally reconstructured radiograph (DRR) images corresponding to the field angle in the planning system were collected as floating images to verify the accuracy of the algorithm. In addition, a total of 533 images were collected from 21 cases of lung tumor treatment data for tumor tracking study, providing quantitative results of tumor location changes during treatment. Image similarity was used for third-party verification of tracking results.Results:The algorithm could cope with different degrees (10%-80%) of image missing. In the phantom verification, 86.8% of the tracking errors were less than 3 mm, and 80% were less than 2 mm. Normalized mutual information (NMI) varied from 1.182±0.026 to 1.202±0.027 ( P<0.005) before and after registration and the change of Hausdorff distance (HD) was from 57.767±6.474 to 56.664±6.733 ( P<0.005). The case results were predominantly translational (-6.0 mm to 6.2 mm), but non-rigid deformation still existed. NMI varied from 1.216±0.031 to 1.225±0.031 ( P<0.005) before and after registration and the change of HD was from 46.384±7.698 to 45.691±8.089 ( P<0.005). Conclusions:The proposed algorithm can cope with different degrees of image missing and performs well in non-rigid registration with data missing images which can be applied in different radiotherapy technologies. It provides a reference idea for processing MV images with multi-modality, partial data and poor image quality.
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Objective:To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring.Methods:CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed.Results:The model had a high prediction success rate for the target position. The variance explainded and root mean squared error ( RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion:The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.
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Objective:To propose a machine learning-based markerless beam′s eye view (BEV) tumor tracking algorithm that can be applied to low-quality megavolt (MV) images with multileaf collimator (MLC)-induced occlusion and non-rigid deformation.Methods:This study processed the registration of MV images using the window template matching method and end-to-end unsupervised network Voxelmorph and verified the accuracy of the tumor tracking algorithm using dynamic chest models. Phantom QA plans were executed after the treatment offset was manually set on the accelerator, and 682 electronic portal imaging device (EPID) images obtained during the treatment were collected as fixed images. Moreover, the digitally reconstructed radiography (DRR) images corresponding to the portal angles in the planning system were collected as floating images for the study of target volume tracking. In addition, 533 pairs of EPID and DRR images of 21 lung tumor patients treated with radiotherapy were collected to conduct the study of tumor tracking and provide quantitative result of changes in tumor locations during the treatment. Image similarity was used for third-party validation of the algorithm.Results:The algorithm could process images with different degrees (10%-80%) of data missing and performed well in non-rigid registration of images with data missing. As shown by the phantom verification, 86.8% and 80% of the tracking errors were less than 3 mm and less than 2 mm, respectively, and the normalized mutual information (NMI) varied from 1.18 ± 0.02 to 1.20 ± 0.02 after registration ( t = -6.78, P = 0.001). The tumor motion of the clinical cases was dominated by translation, with an average displacement of 3.78 mm and a maximum displacement of 7.46 mm. The registration result of the cases showed the presence of non-rigid deformations, and the corresponding NMI varied from 1.21 ± 0.03 before registration to 1.22 ± 0.03 after registration ( t = -2.91, P = 0.001). Conclusions:The tumor tracking algorithm proposed in this study has reliable tracking accuracy and high robustness and can be used for non-invasive and real-time tumor tracking requiring no additional equipment and radiation dose.
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Objective:To propose a method of image similarity measurement based on structure information and intuitionistic fuzzy set and measure the similarity between CT image and CBCT image of radiotherapy plan positioning, aiming to objectively measure the setup errors.Methods:A total of four pre-registration images of a nasopharyngeal carcinoma patient on the cross-sectional and sagittal planes and a pelvic tumor patient on the cross-sectional and coronal planes were randomly selected. Five methods were used to quantify the setup errors, including correlation coefficient, mean square error, image joint entropy, mutual information and similarity measure method.Results:All five methods could describe the deviation to a certain extent. Compared with other methods, the similarity measure method showed a stronger upward trend with the increase of errors. After normalization, the results of five types of error increase on the cross-sectional plane of the nasopharyngeal carcinoma patient were 0.553, 0.683, 1.055, 1.995, 5.151, and 1.171, 1.618, 1.962, 1.790, 3.572 on the sagittal plane, respectively. The results of other methods were between 0 and 2 after normalization, and the results of different errors of the same method slightly changed. In addition, the method was more sensitive to the soft tissue errors.Conclusions:The image similarity measurement method based on structure information and intuitionistic fuzzy set is more consistent with human eye perception than the existing evaluation methods. The errors between bone markers and soft tissues can be objectively quantified to certain extent. The soft tissue deviation reflected by the setup errors is of significance for individualized precision radiotherapy.
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Objective@#To quantitatively evaluate dose accuracy of radiotherapy for cervical cancer.@*Methods@#A CT image correction algorithm based on image transformation was proposed. Referring to CBCT images, CT images of radiotherapy plan for cervical cancer were corrected to obtain the corrected images which could reflect the actual body position of treatment. The clinical plan was transplanted to the corrected images for dose recalculation as a test plan, and the dosimetry parameters were statistically compared to evaluate the dose accuracy.@*Results@#Both of the target coverage of contrast plans could meet the clinical requirements (>98%), and there was no significant difference in the homogeneity index (P=0.150). The conformability of the test plan was significantly worse than that of the clinical plan (P<0.05). The maximum dose of each organ at risk in the test plan was approximately 30 cGy higher than that of the clinical plan (P<0.05), V50 was slightly higher than that of the clinical plan, whereas the average dose (Dmean) did not significantly differ.@*Conclusion@#The CT image correction algorithm based on image transformation can quantitatively evaluate the dose accuracy of radiotherapy for cervical cancer, which provides reference for resolving similar problems in clinical practice.
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Objective To examine the application of On-Board Imaging (OBI) system-based image-guided radiotherapy (IGRT) in the improvement of the precision of intensity-modulated radiotherapy (IMRT) for nasopharyngeal carcinoma.Methods Ten patients with nasopharyngeal carcinoma were treated with IMRT using the OBI system. The IGRT images after positioning, position adjustment, and treatment were observed and recorded to investigate the image difference between CT simulation and IGRT. Results The deviations in the x (lateral), y (cranial-caudal), and z (ventral-dorsal) directions between CT simulation and IGRT images were 0.22±1.00 mm,-0.37±1.28 mm, and 0.04±1.36 mm, respectively, after positioning, 0.29±0.76 mm,-0.04±0.78 mm, and -0.01±0.92 mm, respectively, after position adjustment, and 0.20±0.78 mm, 0.16±0.80 mm, and 0.05±0.92 mm, respectively, after treatment. The probabilities of a ≤1 mm deviation in the x, y, and z directions were 81.0%, 77.6%, and 88.2%, respectively, after positioning, 92.5%, 96.4%, and 96.4%, respectively, after position adjustment, and 91.7%, 94.9%, and 96.8%, respectively, after treatment. Conclusions The application of OBI system-based IGRT is very important in the improvement of the precision of fractionated IMRT for patients with nasopharyngeal carcinoma. The position of the patient should be adjusted based on the IGRT image after positioning in order to correct set-up error and effectively increase the precision of fractionated IMRT.